Originally published on khadim.tech.
In short: China's AI stock bubble burst in 2026: the STAR50 index fell 40% in under four months, erasing about $480 billion, while Chinese AI firms were rationing compute and raising prices. In the US, Microsoft, Alphabet, Amazon and Meta spent $201.9 billion on property and equipment in the third quarter of 2026, $26.4 billion more than their combined operating cash flow. A few forecasters put a US market fall in 2027 or 2028, while the other strategists Bloomberg tracks see about 14% upside on average. For a company using AI, the main risk in a correction is a vendor failing, so keep a second provider, an open-weight model you can run yourself, and a measured return for each use case.
The AI bubble burst in China in 2026, on the stock market. China's STAR50 technology index fell 40% in under four months, at a time when Chinese AI companies were short of computing power and raising their prices. Valuations collapsed, and demand for AI did not.
In the US the argument is about money. Microsoft, Alphabet, Amazon and Meta now spend more on data centres and equipment than their businesses generate in cash, and they are borrowing and selling shares to cover the gap. A minority of forecasters name 2027 or 2028 for a correction, and the strategists Bloomberg tracks still see US stocks rising on average.
For a company that uses AI, the useful question is what a fall would do to the models and vendors it depends on. The market predictions below are the forecasters' own, and none of this is investment advice.
Did the AI bubble burst in China?
China's AI stock bubble did. On October 27, 2026, Reuters reported that the STAR50, the main index of Shanghai's technology board, had fallen 40% in less than four months, wiping out about $480 billion of market value. At its peak it traded at nearly 250 times the previous 12 months' earnings, and after the fall it was still around 100 times, against 30 times for the Nasdaq 100.
By Goldman Sachs' count, AI stocks are 42% of China's market value and 57% of its trading turnover. The broader CSI 300 was about 20% below its June peak, while the Nasdaq was about 6% below its record.
Beijing helped let the air out. Regulators cracked down on leveraged and high-frequency trading, state-backed investors sold into rallies, and October guidelines from the Communist Party and the State Council set out to "curb investment bubbles" while keeping the "AI Plus" push to use AI in traditional industries.
When MiniMax listed in Hong Kong in January, its retail tranche was oversubscribed 1,848 times, and by late February it and Zhipu, which listed a day earlier, were worth $63 billion together, with enterprise values of more than 100 times the revenue analysts forecast for the next 12 months. A Reuters Breakingviews column compared them with the electric-car maker Nio, whose shares rose tenfold and then fell more than 90%.
What happened to China's AI startups before the crash?
In 2024 more than 144 companies registered large language models with the Cyberspace Administration of China. By the end of that year only about 10% were still investing in large-scale training, according to the Economic Observer, as cited by MIT Technology Review. A price war in May 2024 squeezed them further: ByteDance priced its Doubao Pro model at what it said was 99% below the industry average, and Alibaba cut some Qwen prices by up to 97% (Fortune).
Why did China's AI data centres sit idle?
More than 500 data-centre projects were announced in 2023 and 2024. The Chinese outlets Jiazi Guangnian and 36Kr reported that up to 80% of the newly built computing capacity was unused, and the monthly rent for a server with eight Nvidia H100 GPUs fell to 75,000 yuan from highs of around 180,000 (MIT Technology Review, March 2025). Many were remote sites built for training, and they lost their appeal when demand moved to serving reasoning models close to users. In May 2026 the Australian Strategic Policy Institute still reported utilisation as low as 20% to 30% at some western data centres, against a goal of more than 60%.
Why did AI demand in China keep growing?
By early 2026 the shortage ran the other way. In January Bloomberg reported that Zhipu was limiting access to its coding agent, cutting the daily quota of new coding-plan sales to 20% of its previous level. In April, Caixin reported that a shortage of computing power was forcing Chinese AI firms to "throttle services, ration access and hike prices". Alibaba Cloud raised AI compute prices by 5% to 34% and Baidu by about 5% to 30%, both from April 18 (TrendForce).
The large platforms kept building: Alibaba, Tencent and Baidu spent about 131.8 billion yuan on capital projects in the second quarter of 2026, about 2.1 times as much as a year earlier (36Kr).
China is an imperfect template for the US: the Chinese state actively cooled the market, and the AI crash came on top of a wider fall in Chinese shares.
Is AI a bubble in the US?
In the US, the question is whether spending has outrun cash. Microsoft, Alphabet, Amazon and Meta spent $201.9 billion on property and equipment in the July-to-September quarter of 2026, against $175.5 billion of operating cash flow. A year earlier they spent $97.3 billion against $159.0 billion. Their spending roughly doubled while their cash from operations rose about 10%.
Chart: Q3 2026 capital spending vs operating cash flow for Microsoft, Alphabet, Amazon and Meta, from each company's results.
The figures are reported purchases of property and equipment and net cash from operations for the quarter ended September 30, 2026 (Microsoft's fiscal first quarter of 2027). None of the four reports an AI-only figure, and Oracle is left out because its quarter ends in August.
The case for a bubble
Three of the four are spending more cash than they make. Alphabet's free cash flow was -$9.1 billion in the quarter, and Meta's was -$2.1 billion, against +$10.6 billion a year earlier. Amazon's was -$42.4 billion over the 12 months to September, against +$14.8 billion a year before. Microsoft had about $18.6 billion of operating cash left after its capital spending.
Outside money is filling the gap. Meta announced a $95 billion share offering, its first new stock since 2013 (Semafor). A Bank of Korea report found that Alphabet, Microsoft, Amazon, Meta and Oracle issued $230 billion of bonds in the first nine months of 2026, more than double their total for 2025 (Herald Business).
The IMF warned in October 2026 that "faltering AI investment and profitability could challenge optimistic expectations and trigger a correction in asset prices." It reviewed 14 circular financing deals since 2023, in which AI companies' investors are also their suppliers or customers: about $300 billion of equity and $650 billion of debt, tied to $1.4 trillion of future revenue commitments. Trouble at one recipient could spread along that chain, though the IMF presents this as a vulnerability, not a forecast.
Bain estimated in September 2025 that AI needs $2 trillion of annual revenue by 2030 to pay for the computing it expects to use, and that the world is still $800 billion short after counting AI-driven savings. Citadel's Ken Griffin said on October 20, 2026 that investors are "almost certainly" in an AI bubble, and compared GPUs to airline seats: once supply exceeds demand, prices can collapse.
The case against a bubble
The demand shows up in revenue. In the same quarter Azure revenue grew 48%, Google Cloud grew 103% to $30.7 billion with $11.1 billion of operating income, and AWS grew 39% to $46.0 billion. Microsoft's commercial remaining performance obligation, the revenue under contract that it has not yet recognised, rose 73% to $677 billion. Nvidia's data-centre revenue was $89.0 billion in the quarter to July 26, 2026, up 117% (Nvidia).
Compute is still scarce. AWS raised prices for EC2 Capacity Blocks for ML, its reserved GPU capacity, by about 15% in early January 2026, saying the change "reflects the supply/demand patterns we expect this quarter" (The Register). Jeff Bezos, who called AI "a kind of industrial bubble" in October 2025, said such bubbles are "not nearly as bad" as financial ones (Fortune).
Will the AI bubble burst in 2027?
A handful of forecasters name 2027, and they predict different things:
| Who | What they predict | By when |
|---|---|---|
| Capital Economics (James Reilly) | A "late-stage bubble": the S&P 500 ends 2026 at 8,250, then falls 21% to 6,500 | End of 2027 |
| Joachim Klement, Panmure Liberum | The AI bubble "will either burst in 2027 or in 2028"; the S&P 500 at 5,000, about 36% below its October 2026 level | End of 2027 |
| Ohsung Kwon, Wells Fargo | Markets start treating 2027 as a potential peak year for AI capital spending, which will "likely slow meaningfully in 2028" | 2027 to 2028 |
| Pantheon Macroeconomics | Hyperscaler capital spending growth "looks set to moderate" | 2027 |
| Michael Burry | Reportedly bought put options, which pay off if a share falls, on Micron and Nebius | Expiring June 2027 |
| Gartner | More than 40% of agentic AI projects cancelled | End of 2027 |
Only the first two forecast a market fall in 2027, and Burry's positions bet the same way; the other calls are about slower spending or cancelled projects.
Most strategists disagree: the seven others Bloomberg tracks alongside Klement see 14% upside for US stocks on average. Ruchir Sharma of Rockefeller International names a trigger rather than a date: in a Financial Times column he wrote that the bubble could pop if the 10-year US Treasury yield "decisively breaches" 5%. In mid-September it reached 4.97%.
What happens to companies using AI when the AI bubble bursts?
A correction is a fall in share prices and spending plans. Trained models and finished data centres keep running. What changes for a company using AI is the price of compute, which vendors survive, and how budgets are judged.
On price, the evidence points both ways. Griffin's airline-seat comparison implies that a glut of GPUs would push compute prices down, and the price of a fixed level of LLM capability has fallen between 9 and 900 times a year depending on the task, according to Epoch AI. But shortages pushed prices up in 2026 in both the US and China, so an AI budget should allow for movement in either direction.
The bigger exposure is vendors. Builder.ai, once valued at over $1 billion, backed by Microsoft and funded with more than $450 million, entered insolvency proceedings in May 2025. Gartner estimates that only about 130 of the thousands of companies selling "agentic AI" are real, and calls the relabelling of chatbots and automation tools "agent washing".
Gartner's cancellation forecast names rising costs, unclear business value and weak risk controls as the causes. MIT's Project NANDA, based on 52 interviews and 153 survey responses, found that just 5% of integrated generative AI pilots were extracting millions in value, while the large majority had no measurable effect on profit and loss (Virtualization Review).
How do you protect an AI project from a bubble burst?
I recommend these engineering hedges whatever the market does, because providers change prices, models and terms in good years too.
Keep the model behind one interface
Call the model through one thin layer in your own code, keep prompts and evaluation sets in your repository, and run the same evaluation against a second provider on a schedule. Switching then takes a configuration change and a test run. For agents, tools built as MCP servers can be called by any compatible model client, so the model can change without rebuilding them. That is one reason MCP servers are part of my AI agent consulting work.
Keep an open-weight model you can run yourself
An open-weight model you host stays available if its maker fails or changes its terms. DeepSeek V4, released in April 2026 under the MIT licence, can be served by any provider or on your own hardware, and its Flash version costs $0.14 per million input tokens through DeepSeek's API (Simon Willison). Few companies have this fallback ready: Menlo Ventures found open-source models handled 13% of enterprise AI workloads in mid-2025, down from 19% six months earlier, partly because they trail the best closed models by nine to 12 months. For one narrow task, test whether a smaller open model fine-tuned on your own examples meets your accuracy bar, which is the first step in LLM fine-tuning and consulting.
Running a model yourself means owning the runtime as well. When Ollama 0.34.1 dropped support for LoRA adapters in September 2026, fine-tunes served that way needed a new route, and I tested the two that work in Ollama no longer supports LoRA adapters. Pin the versions you depend on and test upgrades before they reach production.
Measure the return of each use case
Give every AI use case a baseline and a number, such as minutes per task, error rate or cost per thousand requests, and measure before and after. Unclear business value is one of the causes Gartner gives for its cancellation forecast, and a measured result answers it.
Write the exit into the contract
Avoid long prepaid commitments to loss-making start-ups, or keep them small enough to write off. In any AI contract, ask for:
- export of your data, prompts, logs and any fine-tuned weights in a standard format, at any time;
- notice before price changes (AWS raised its reserved GPU prices by about 15% on a Saturday in January 2026);
- a termination right and the return of your data if the vendor becomes insolvent or is acquired;
- clear ownership of anything trained on your data.
FAQ
Did the AI bubble burst in China?
China's AI stock bubble did. The STAR50 index fell 40% in under four months to late October 2026, wiping out about $480 billion, according to Reuters. Demand for AI computing held up: earlier in 2026, Chinese cloud providers raised AI compute prices by 5% to 34% and some AI firms rationed access.
Is AI a bubble?
Forecasters disagree. On the spending side, Microsoft, Alphabet, Amazon and Meta put $26.4 billion more into property and equipment than they generated in operating cash in the third quarter of 2026, and the IMF warns that faltering AI investment could trigger a correction in asset prices. On the revenue side, Google Cloud grew 103% in the same quarter and Microsoft reported $677 billion of contracted commercial revenue not yet recognised.
Will the AI bubble burst in 2027?
A few forecasters say so. Capital Economics expects the S&P 500 to fall 21% to 6,500 by the end of 2027, and Panmure Liberum's Joachim Klement says the AI bubble will burst in 2027 or 2028. They are in the minority: the seven other strategists Bloomberg tracks see about 14% upside for US stocks on average.
What happens when the AI bubble bursts?
Share prices and spending plans fall, but models already trained keep running. For a company using AI, the larger exposure is its vendors: Microsoft-backed Builder.ai, once valued at over $1 billion, entered insolvency proceedings in May 2025. Projects with a measured return are the easiest to defend when budgets tighten.
Are big tech companies spending more than they earn on AI?
In cash terms, three of them were in the third quarter of 2026. Alphabet, Amazon and Meta each spent more on property and equipment than their operating cash flow, while Microsoft spent $39.4 billion against $58.0 billion. The filings report total capital spending, with no AI-only figure.
How can a company protect its AI projects if the bubble bursts?
Call models through one interface so you can switch providers, and keep a tested open-weight model you can run yourself. Measure each use case against a baseline, and avoid long prepaid commitments to loss-making start-ups. Contracts should cover data export, notice of price changes and what happens if the vendor becomes insolvent.
Disclosure: written with AI assistance, from my own research and notes. Not investment advice.
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